Automated Diagnosis-Assisting Medical Devices

ABSTRACT

A system includes an electronic stethoscope producing a quasi-periodic signal, a processor, and a memory device with stored instructions that, when executed by the processor, cause the system to receive a representation of the quasi-periodic signal, to remove a DC component from the received representation of the quasi-periodic signal to produce a purely time-varying signal, and to filter, the time-varying signal to produce a pre-processed signal. A portion of the pre-processed signal is auto-correlated with itself, and a corresponding auto-correlation output is stored. A biphasic tapering function is applied to the auto-correlation output and produces a first maximum, the function including a time constant parameter that is a function of the quasi-periodic signal. A representation is stored, based on the first maximum, as an indication of a rate or frequency of the quasi-periodic signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/210,026, titled “Automated Diagnosis-Assisting Medical DevicesUtilizing Pattern Localization Of Quasi-Periodic Signals,” filed Mar.13, 2014, which claims the benefit of and priority to U.S. ProvisionalPatent Application No. 61/787,998, titled “Automated Diagnosis-AssistingMedical Devices Utilizing Rate/Frequency Estimation And PatternLocalization Of Quasi-Periodic Signals” and filed on Mar. 15, 2013, eachof which is incorporated herein by reference in its respective entirety.

COPYRIGHT

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patentdisclosure, as it appears in the Patent and Trademark Office patentfiles or records, but otherwise reserves all copyright rightswhatsoever.

FIELD OF THE INVENTION

Various aspects of the present disclosure relate to the estimation ofthe rate or frequency and to the localization of similar patterns inquasi-periodic signals. More specifically, for example, the signals arenot limited to being quasi-periodic and are often overlaid with noise orother artifacts. More generally, some aspects relate to automated signalprocessing of sounds originating from various body structures forproviding clinical referral conditions at a site, such as the patient'ssite.

BACKGROUND

Analyzing quasi-periodic signals is very common, e.g., analyzing soundsoriginating from the heart or lungs, and has long been a tool forevaluating conditions of subjects or patients. Since the existence ofthe stethoscope, the electro-cardiogram device, and similar devices,such practices have been formalized. In the case of the stethoscope, forexample, the sound is detected non-invasively at the surface of the skinand evaluated by a skilled practitioner. This is a standard screeningmethod performed worldwide and called auscultation. Interestingly,auscultation is also one of the few remaining routine medical proceduresin which the diagnosis is made purely by the medical professional wholistens and interprets the sounds originating from the heart or lungsbased solely on his or her training and experience.

With no objective and comparable means of evaluation, the quality of thesubjective human diagnosis is solely dependent on the investigatingmedical professional, inevitably leading to a lack of objectivity and ahigh variability of findings between medical professionals. As such,making a diagnosis is vulnerable to human error and subjectivity due toa multitude of potential causes (e.g., stressful working environment,lack of sleep, etc.) associated with the medical profession. All of theabove identified problems create a huge burden of responsibility for themedical professionals. Additionally, without an independent system inplace that supports and documents the medical professionals' findingsfrom the auscultation (including objective, clinically tested,investigator-independent, patient-specific parameters), the medicalprofessionals lack an objective basis to defend their subjectivefindings.

Since the rise of high-speed computing, increasing attention has focusedon analyzing digitized quasi-periodic signals through digital signalprocessing (“DSP”) techniques. Often, the DSP techniques have been usedsimply for determining the rate, frequency, or steadiness of suchsignals. More recently, the DSP technique have also been used todetermine pathological conditions of a medical subject.

The problem or challenge with such analyses lies in the reliableextraction and classification of significant features, often hidden inthe recordings of such variable biological signals. This leads to theimportance of proper or correct signal segmentation, which is oftenperformed manually or sometimes automatically on good quality signals.In reality, recorded biological signals in a clinical environment arenot of “good” quality, in which case existing systems struggle to yieldreliable and robust results. For example, most current approaches ofrate detection are simply triggered by the presence of a certain energylevel in the signal, which is problematic in environments containingother noise or sounds, such as, for example, people walking or talking,other machines, traffic noise, etc. Other approaches are alsodetrimental because they require external input, such as, for example,electro cardiogram data, to achieve correct signal segmentation.

Therefore, there is a need, for example, for handling uncorrelated noiseand variations in the periodicity of such variable input signals, and/orfor estimating signal rates or frequencies, as well as recognizing andidentifying locations of similar patterns. For example, such technologycan be utilized as a standalone device or as part of a larger system forautomated diagnoses of quasi-periodic signals.

SUMMARY OF THE INVENTION

According to an aspect of the present disclosure, a method or algorithmis disclosed that estimates a rate or frequency of quasi-periodicsignals and localizes similar patterns in quasi-periodic signals withoutrequiring high-end computing powers. Quasi-periodic signals are signalsthat can potentially be highly irregular while still containing somerepeating, often hidden, features. Exemplary signals include biologicalsignals that are concealed by noise and artifacts from various sources,such as originating internally (from a body structure) or externally(from the environment), and that are independent of the signal type oracquisition (e.g., electrical, mechanical, optical, acoustical, etc.).The signals are typically, but not necessarily limited to beingquasi-periodic, and are often overlaid with noise or other artifacts.

Another aspect of the present disclosure is directed to determining arepresentative estimation of the rate of such signals, e.g., heart beatfrequency or breathing frequency. The signal rate from biologicalsources is often a parameter of interest in clinical settings, but canalso be utilized in subsequent or related signal processing stages toperform further analysis. The algorithm includes utilizing a combinationof auto-correlation functions, tapering functions, and/or progressivesignal splitting and statistical tools to analyze quasi-periodicsignals.

Furthermore, yet another aspect of the present disclosure utilizessignal templates, e.g. a single representative heartbeat in a series ofheartbeats or an analytical signal that shows similar features as thepattern of interest in the target signal, to search throughout theentire signal for locations where similar signal patters (or shapes) arefound. The resulting locations are stored and returned. The algorithmutilizes a sequence of cross-correlation and windowing functions incombination with signal rate estimation that makes the algorithm robustagainst changes in periodicity, noise, and artefacts.

According to yet another aspect of the present disclosure, a method oralgorithm is disclosed that changes traditional functions of electronicstethoscopes from a device typically capable of recording, storing, andmanipulating data, into a device that automatically delivers diagnosticresults for clinically relevant referral conditions directly topatient's site. By utilizing, for example, parallel system processing,involving novel algorithms, and physiological parameters that areoptimized with findings from clinical studies, an embodiment of thepresent disclosure relates to a method of analyzing and diagnosingdigital physiological signals that were recorded with commerciallyavailable electronic stethoscopes.

According to yet another aspect of the present disclosure, a method ofestimating a frequency of a quasi-periodic signal is performed directlyby an electronic stethoscope as illustrated in FIG. 3 and FIG. 4.Alternatively, the method is performed by a small, external and portabledevice connected or linked to the stethoscope, as illustrated in FIG. 5(e.g., a small device, tablet, smartphone, etc.), but that does notrequire high-end computing powers. The utilization of one or moreaspects of the disclosed algorithm using standard computing resources,e.g., found in state-of-the-art smartphones or tablet computers, ispossible due to various attributes of the algorithm, such as furtherdescribed below.

According to one attribute, the algorithm uses methods likeauto-correlation or cross-correlation, which can be computed veryefficiently by using time-frequency conversion to perform suchoperations. Microprocessors often provide optimized implementations ofsuch time-frequency conversions, such as Fast Fourier Transform (“FFT”),and, therefore, significantly boosting time-domain operations.

According to another attribute, the algorithm enables fast and efficientcomputation by using pre-trained classifiers (e.g., neural networks,support vector machines, Bayesian networks, etc.). The pre-trainedclassifiers facilitate new data to be classified with simple andcomputationally efficient operations (e.g., matrix multiplications). Forthis approach, parameters are determined with training data. Forexample, in reference to neural networks, weights and biases determinedwith training data. Or, in reference to support vector machines, thelocation of the support vectors in the hyperspace is determined withtraining data. Comprehensive and well classified training data is usefulfor a good pre-training of classifiers. The training data of thedisclosed algorithm includes, for example, raw phonocardiogram dataand/or corresponding diagnoses (obtained using, for example,echocardiography as the gold standard method for diagnosing heartdefects). With a comprehensive training set, a classifier can beoptimized (or, pre-trained) and applied to new data, which enables fastand efficient computations. In contrast, so-called lazy-learning methodsuse the whole available data set (stored locally) and compare new dataagainst the whole training set for classification. The lazy-learningmethods lead to higher space requirements for storing the training dataset and/or to increased computational costs for performing theclassification.

According to yet another attribute, the features of interest (or inputsto the classifier) are determined in advance by feature selectionalgorithms (e.g., sequential floating forward selection), which reducefeature space. Features are also analyzed using statistical tools suchas a principal component analysis, which results in linearlyuncorrelated variables and which further optimizes the feature space.Hence, only the most powerful and meaningful features are selected forthe algorithm, increasing its computational efficiency and robustnessagainst noise and outliers.

According to yet another aspect of the present disclosure, a methodand/or system includes combining an electronic stethoscope with aportable device (FIG. 5) for automated analysis and diagnosis-supportfor stethoscope-based auscultation. The method and/or system utilizesone or more of the algorithms described below in reference to FIGS. 1,2, and/or 6. The analysis is performed by the portable device, whichprovides results including a set of patient-specific parameters and/orindicators. The results are investigator independent and include medicaland technical parameters, such as heart and/or breathing rate, heartand/or breathing rate variability, systolic and diastolic energy, signalcurve, diagnosis suggestion, etc. At least some of these objectiveparameters and/or other results are displayed and/or stored on theportable device as a means for diagnosis support for the medicalprofessional or other user.

According to yet another aspect of the present disclosure, abidirectional system architecture is illustrated in FIG. 7 for enablinga device to be utilized for one or more of the following purposes:

-   -   (i) documentation purposes including, for example, saving all        data and results in a common file format (e.g., PDF format),        printing, emailing, bidirectional integration into a hospital        information system (“HIS”), and/or efficient filing of all data        and results to a patient's medical file;    -   (ii) teaching, training, research, and/or presentation purposes        by wirelessly connecting the portable device to a single or        multiple other portable devices that receive all data, including        the results obtained with the utilization of one or more of the        described algorithms; and/or    -   (iii) bidirectional tele-auscultation for remote auscultation        allowing the user to remotely control settings of an electronic        stethoscope at a patient's site (e.g., change filters, adjust        volumes, etc.), to communicate with a person operating the        electronic stethoscope (e.g., instructing the person to change        the position of the stethoscope), and to further provide        documentation and HIS integration.

According to yet another aspect of the present disclosure, abidirectional system architecture is disclosed as illustrated in FIG. 7and in which the HIS is utilized to host or run one or more of thedescribed algorithms. The system allows a user to access the system viaa portable device or any computer connected to the HIS. Optionally,recorded signal data is uploaded and/or stored in the HIS. The data isanalyzed by the HIS directly and/or the data is downloaded onto theportable device for later or remote analysis.

According to other aspects of the present disclosure, the device doesnot require any external input from medical professionals or otherdevices (e.g., ECG), does not require traditional auscultationtechniques to be modified, does not require especially quietenvironments (such as, for example, the holding of breath by the patientduring auscultation), and/or does not require manual or semi-automatedanalysis by a medical professional. Alternatively, adding an externalinput by the user is optional and does not hinder the device fromperforming its tasks. In fact, the external input might potentially evenincrease the accuracy of the results.

By way of example, in reference to a phonocardiogram analysis, the ageof the patient is a helpful parameter for narrowing down the range oflikely heart rates and possible diseases (e.g., a specificclassification of a heart murmur). A newborn, for example, usually has aheart rate greater than 100 beats per minute and the range of possiblediseases is generally different than, for example, for a child greaterthan 2 years of age. One or more features of the present disclosure arebeneficial to and enhance existing electronic stethoscopes by increasingtheir function as a medical device and informing medical staff within ashort period of time whether physiological signals are healthy orrequire further medical attention. Thus, one or more features of thepresent disclosure can be utilized directly on an electronic stethoscopeor in combination with an electronic stethoscope and a portable device,wherein computations and interaction (e.g., visualization of thefindings) with medical staff are achieved through the portable device.

According to one embodiment of the present disclosure, a system forprocessing a quasi-periodic signal includes an electronic stethoscopethat produces a quasi-periodic signal, a processor, and a memory devicewith stored instructions that, when executed by the processor, cause thesystem to receive a representation of the quasi-periodic signal. A DCcomponent is removed from the received representation of thequasi-periodic signal to produce a purely time-varying signal, and thetime-varying signal is filtered to produce a pre-processed signal. Atleast a portion of a representation of the pre-processed signal isauto-correlated with itself, and a corresponding auto-correlation outputis stored for the at least portion of the representation of thepre-processed signal. A biphasic tapering function is applied to theauto-correlation output, the tapering function including a time constantparameter that is a function of the quasi-periodic signal and producinga first maximum. A representation is stored, based on the first maximum,in the memory device as an indication of a rate or a frequency of thequasi-periodic signal.

According to another embodiment of the present disclosure, a system forprocessing a quasi-periodic signal includes an electronic stethoscopethat produces a quasi-periodic signal, a processor, and a memory devicewith stored instructions that, when executed by the processor, cause thesystem to, in response to receiving a representation of thequasi-periodic signal, produce a pre-processed time-varying signal. Anauto-correlation output is produced that corresponds to anauto-correlation of at least a portion of a representation of thepre-processed time-varying signal with itself. In response to applying abiphasic tapering function to the auto-correlation output, a frequencyof the quasi-periodic signal is estimated, and a search window isdefined based on the estimated frequency of the quasi-periodic signal. Astarting position is defined in the received quasi-periodic signal, thestarting position corresponding to a first maximum. A portion of thequasi-periodic signal is cross-correlated in the search window with atemplate signal pattern to be matched to produce a second maximum thatis defined by the controller as a new starting position, which isstored.

The foregoing and additional aspects and embodiments of the presentdisclosure will be apparent to those of ordinary skill in the art inview of the detailed description of various embodiments and/or aspects,which is made with reference to the drawings, a brief description ofwhich is provided next.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of exemplary implementations of the presentdisclosure will become apparent from the description, and theaccompanying drawings. According to common practice, various features ofthe drawings are not to scale but are purposefully modified arbitrarilyfor improved clarity wherein:

FIG. 1 is a diagrammatic illustrating a process of rate or frequencyestimation in quasi-periodic signals.

FIG. 2 is a flowchart illustrating a process for localization of similarpatterns in quasi-periodic signals.

FIG. 3 is a representative illustration of an electronic stethoscopethat includes a display for visual indication of a resulting diagnosis.

FIG. 4 is a representative illustration of an electronic stethoscopethat acoustically indicates a resulting diagnosis.

FIG. 5 is a representative illustration of an electronic stethoscopethat is connected to an external, portable device (e.g., a small device,tablet, smartphone, etc.) for indication (visually, acoustically, orotherwise) of resulting findings including diagnosis suggestion.

FIG. 6 is a flowchart illustrating an exemplary method of digital signalprocessing of physiological signals.

FIG. 7 outlines a bidirectional system architecture for achievingdocumentation, teaching, and/or bidirectional tele-auscultation.

DETAILED DESCRIPTION OF THE INVENTION

Referring to FIG. 1, a diagrammatic illustrates a rate/frequencyestimation algorithm in accordance with one aspect of the presentdisclosure. At 101, a quasi-periodic input signal, such as an acousticalsignal indicative of a physiological rhythm (e.g., heartbeat,respiration), is loaded. At 102, a DC component is removed from theinput signal, s, according to s_(DCrem)=s−mean(s), where

${{mean}(x)} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}{x(n)}}}$

is the mean operator, s_(DCrem) is the input signal having its DCcomponent removed, and N is the length of x.

At 103, filtering of the input signal is applied to produce apre-processed signal that emphasizes the quasi-periodic patterns of thesignal for rate estimation (e.g., the heart sound S1 and S2 in aphonocardiogram). The filtering is performed with a standard band-passfilter (high-pass filtering and/or low-pass filtering) or with waveletfiltering. In accordance with wavelet filtering, the signal isdecomposed into detail and approximation coefficients, and, as such,thresholding of the detail coefficients with subsequent reconstructionof the signal enables noise removal. Corresponding cut-off frequencies,filter types, and threshold levels in 103 are dependent on the type ofinput signal. Examples of input signals include heart sounds, breathingsounds, bowel sounds, quasi-periodic signals, etc. Furthermore,threshold levels are not directly dependent on the type of input signal,but are computed specifically for each input signal.

At 104, from the now pre-processed signal, signal energy is calculatedand normalized, e.g.,

${s_{norm} = {s_{filt}/\sqrt{\frac{1}{N}{\sum\limits_{n = 1}^{N}{s_{filt}(n)}^{2}}}}},$

where the denominator corresponds to the root mean square of the signal,where s_(filt) is the filtered, pre-processed signal, and N denotes thelength of s_(filt). If permitted by signal length, at 105 the signalenergy is split into progressively smaller time domains, by continuouslydividing the entire pre-processed signal energy in halves, thirds,quarters, etc. The splitting of the signal energy continues as long asthe length of the smallest resulting domain contains sufficientinformation for a meaningful analysis.

For example, if a medical professional is interested in analyzingheartbeats, the size of the smallest domains would have to be largeenough to cover the main features of a few heartbeats. Every resultingsignal energy domain is stored accordingly in a memory device of acomputer or computing device.

At 106, for each domain of the energy the auto-correlation of the domainitself is computed and stored, yielding auto-correlations for everydomain. A normalized version of the auto- and cross-correlation is used,which compensates for the differences in signal magnitudes and properlycorrelates a shorter signal with a longer one. This normalized versiondivides the results of the correlation by the energy of the parts of thesignals that were effectively correlated.

First, the shorter signal s₂ is zero-padded to have equal length as thelonger signal s₁. Second, the standard cross-correlation of s₁ and s₂ isperformed by temp=s₁★s₂, where temp includes only the positive terms,i.e., the second half, of the standard cross-correlation (★ is thecross-correlation operator). Third, the masked energy correlation,en_(m), is computed according to en_(m)=s₁ ²★ones(length(s₂)), where thelatter term represents a rectangular window with the length equal to thelength of the shorter signal s₂. Fourth, the result of the normalizedcross-correlation, res_(cc), is computed according to

$\left. {{res}_{cc} = \frac{temp}{{{abs}\left( \sqrt{s\; n_{m}} \right)} + {{abs}\left( \sqrt{s_{2},s_{2}} \right)}}} \right),$

where the dot product s₂·s₂ is used. Since a convolution in the timedomain corresponds to a multiplication in the frequency domain,efficient computation is achieved, e.g., computing auto-correlation ofs(t), res_(cc)=IFFT(F_(s)(f)*F_(s)*(f)), where F_(s)(f)=FFT(s(t)) is theFFT of s(t), F_(s)*(f) denotes the complex conjugate, and IFFT performsthe Inverse Fast Fourier Transform.

At 107, a tapering function is applied to every auto-correlation toamplify the relevant maximum in the auto-correlation. To amplify thefirst maximum representing the rate or frequency, the tapering functionis biphasic, where the two phases are selected depending on the inputsignal but can generally include a rising edge followed by a trailingedge. The biphasic function is necessary because the auto-correlationfunction of quasi-periodic signals features multiple peaks, and thebiphasic nature of the tapering function allows the selection of themost probable (single) peak (by tapering other, more improbable peaks)representing the period of interest. The tapering function additionallyincludes a time constant as a parameter that also depends on the inputsignal and that is pre-determined from, for example, values reported inthe literature (e.g., average breathing/heart rates for differentpatient groups) or suitable clinical data if available.

An example of a biphasic tapering function, f_(taper)(t), is thefollowing exponential function

${{f_{taper}(t)} = {\frac{t}{T_{c}}^{- \frac{t}{T_{c}}}}},$

where T_(c) is a time constant and t is the time. The two phases of thetapering function are reflected in the first term, which represents alinear increase (e.g., a rising edge or phase), and the second termrepresenting an exponential decline (e.g., a trailing edge or phase).Although other biphasic tapering functions could be used to amplify themaximum in the auto-correlation representing the period time, theexemplary exponential form described above is suitable for estimating afrequency specific in phonocardiograms, based on determinations ofclinical phonocardiogram data covered in substantial amount of noise.

The exemplary biphasic tapering function yielded the best results forselecting the single peak in the auto-correlation function thatcorrelates best with the heart rate of the patient. The maximum of thisparticular tapering function is t=T_(c) (as can be seen by setting thefirst derivative of f_(taper) to zero,

$\left. {f_{taper}^{\prime} = {{\frac{T_{c} - t}{T_{c}^{2}}^{{- t}/T_{c}}} = 0}} \right).$

Furthermore, T_(c) is computed by T_(c)=1/f, where f is the mostprobable frequency in the signal, which is determined from reportedvalues in the literature or clinical data. By way of example, for veryyoung children (during the first few months of life) a time constant inthe range of 0.6 to 0.3 is appropriate and corresponds to an averageheart rate between about 100-200 beats per minute.

At 108, the positions of the maxima in the tapered auto-correlations arecomputed and stored in a memory device. At 109, standard statisticalmeasures such as, for example, mean, median, standard deviation,variance, or other tools (e.g., maximum likelihood estimation) areutilized to determine one representative position for all maxima.Finally, at 110 the one representative position of all maxima of thetapered auto-correlations is converted, yielding the representativesignal rate or signal frequency.

Referring to FIG. 2, a flowchart is directed to outlining a process forthe localization of similar patterns in quasi-periodic signals. Thisprocess does not require any external input, such as an ECG signal forsegmentation or other purposes. The localization algorithm calls for atemplate representing a signal pattern to be matched to similar patternsthroughout the signal. For example, in a physiological signal of aseries of heartbeats, the template can be one of the heartbeats in theseries.

The template can also be an analytical signal that shows similarfeatures as the pattern of interest in the target signal. For example,for a phonocardiogram, a primitive template includes two waveformsrepresenting S1 and S2 that are shifted in time, depending on theestimated heart rate. Examples of such waveforms that feature certainsimilarities with S1 and S2 include the Shannon wavelet

${\Psi_{shannon}(t)} = {\sin \; {c\left( \frac{t}{2} \right)}{\cos \left( \frac{3\pi \; t}{2} \right)}}$

and the real part of the Morlet wavelet

${{\Psi_{morlet}(t)} = {c_{\sigma}\pi^{- \frac{1}{4}}{^{- \frac{t^{2}}{2}}\left( {^{{\sigma}\; t} - \kappa_{\sigma}} \right)}}},$

where c_(σ) and x_(σ) are constants.

At 201, the template is cross-correlated with the entire input signal,such as a physiological signal. The maximum of the cross-correlationrepresents the best match of the template with the signal, and at 202the position of the maximum is defined as the starting position S1 forthe localization algorithm.

At 203, the localization algorithm checks if the remaining signal lengthafter the S1 position is long enough to contain the search windowspecified at 204. If yes, the algorithm steps to the right of S1(forward in time) where a new starting point is defined for a searchwindow, shown at 204. The step size, as well as the size of the searchwindow in 204, is based on an estimated signal frequency or signal rate,which, for example, is estimated with the algorithm described in FIG. 1above.

At 205, the windowed part of the signal is cross-correlated with thetemplate. At 206, the position of the maximum in the cross-correlationis computed and stored in a memory device as the new starting pointS_(i). The maximum represents the best match of the template with thesignal within the search window. Next, the localization algorithm goesback to 203 to check if it has arrived close to the signal end. If not,modules 204-206 are repeated. If yes, the algorithm goes back to thestarting position S1, according to 207.

Throughout modules 208-211, the localization algorithm performs the sameoperations as in modules 203-206, but instead of stepping to the rightthe algorithm keeps stepping to the left of S1 (back in time). The stepsize as well as the size of the search window throughout modules 208-211are again based on an estimated signal frequency or signal rate, but arenot necessarily the same as at modules 203-206. Examples of such stepsizes suitable for auscultation data from new-borns are 0.6*T_(p) forthe start and 1.8*T_(p) for the end of the search window to the right,and 1.4*T_(p) and 0.2*T_(p) for the length of the search window, where

$T_{p} = \frac{1}{f}$

is the period.

When the localization algorithm arrives at a position too close to thesignal start (the left end of the signal), where the remaining part ofthe signal to the left is too short to contain a new search window, 208ensures that the localization algorithm jumps to 212, where all storedpositions S_(i) are returned. The positions S_(i) represent thelocations of the patterns throughout the signal that match the template.This process can be repeated for different patterns of the same inputsignal. The input signal is not limited to being quasi-periodic and caninclude a significant amount of noise or artefacts. Furthermore, thisprocess is independent of the type of signal or signal acquisition(e.g., electrical, mechanical, optical, acoustical, etc.).

Advantages of using a search window include that the signal does nothave to be strictly periodic and repeating patterns can still be found.Moreover, the window restricts the search area to a reasonable size,ensuring that patterns covered in noise can also be detected using atemplate containing similar features as the desired pattern. Ultimately,the lengths and positions of the search windows depend on the signal,keeping in mind that longer windows allow the signal to be moreirregular while making the pattern detection in noisy signals morecomplicated. The position and length of the window is selected such thatit does not contain two or more of the patterns of interest, otherwiseone of the multiple patterns might be skipped.

Referring to FIG. 6, a flow chart illustrates modifying an electronicstethoscope into a diagnosis-assisting tool providing additionalmedically relevant information regarding the physiological signal. At601, a quasi-periodical, digital physiological signal is received froman electronic stethoscope. Then, the signal is pre-processed (e.g.filtered, normalized, etc.) at 602.

The beat frequency is estimated at 603 using, e.g., the algorithmillustrated in FIG. 1 and used in the segmentation stage at 604, whichis also illustrated in FIG. 2. This segmentation at 604 yields thesegments of interest of the signal, e.g., the systole and/or diastole ofeach heart beat, and/or the inhale and/or exhale phase of a breath.

In a parallel system processing stage, for example, at 605, variousanalysis modules operating in both the frequency and the time domain areapplied to extract features of the segments obtained at 604. Forexample, such features include an Energy Analysis, Timing Features(e.g., Heart Rate Variability, Duration of S1/Systole/S2/Diastole,etc.), Fourier Transform, Short Time Fourier Transform, Higher OrderStatistics (e.g., Bispectra, Gaussian Mixture Models, etc.), Discreteand Continuous Wavelet Analysis, Fractal Dimension Analysis,Stockwell-Transform, Error Entropy Analysis, etc. The output of theparallel modules at 605 is combined at 606 and passed to 607, where theoutput is used in a decision making stage. If external data, such aspatient age or certain aspects of patient's medical history, is added,classification of specific pathologies might yield increasing accuracy.

The output of 607 is forwarded to 608 where the diagnosis is indicatedto the user on an electronic display device. The diagnosis is indicated,for example, via a binary output, via probabilities, via an acousticsignal, and/or via a visual interface. The visual interface can includea detailed listing of the findings, including a diagnosis suggestion,all of which are optionally stored in a memory device on an electronicstethoscope or shared, stored or printed through other means asdescribed above.

Additionally, still referring to FIG. 6, a basic structure of a digitalsignal processing is utilized by one or more aspects of the invention.Parallelization, in combination with one or more of the above describedalgorithms and in combination with specific parameters obtained throughclinical studies enable a fully automated analysis without requiring anyexternal input in addition to the physiological signal itself (althoughexternal data and/or parameters can be optionally added). The diagnosticresult is revealed directly on the electronic stethoscope (e.g., FIGS.3-5) or the portable device that is connected to the electronicstethoscope (e.g., on display 502 of FIG. 5), either visually (through adisplay 301, 502) or acoustically (e.g., sound emitter 401).

Some or all modules described above, which have been described by way ofexample herein, represent one or more algorithms that correspond to atleast some instructions executed by one or more controllers to performthe functions or modules disclosed. Any of the methods or algorithms orfunctions described herein can include machine or computer-readableinstructions for execution by a processor, controller, computer, and/orany other suitable processing or computing device. Any algorithm,software, and/or method disclosed herein can be embodied as a computerprogram product having one or more non-transitory tangible medium ormedia, such as, for example, a flash memory, a CD-ROM, a floppy disk, ahard drive, a digital versatile disk (“DVD)”, or other memory devices.However, persons of ordinary skill in the art will readily appreciatethat the entire algorithm and/or parts thereof can alternatively beexecuted by a device other than a controller and/or embodied in firmwareor dedicated hardware (e.g., it can be implemented by an applicationspecific integrated circuit (“ASIC”), a programmable logic device(“PLD”), a field programmable logic device (“FPLD”), discrete logic,etc.).

Referring to FIG. 5, illustrates a method or system for automatedanalysis and diagnosis-support for stethoscope-based auscultation. Anautomated analysis and diagnosis-support system 500 includes anelectronic stethoscope 501 with signal transmitting capabilities, whichinclude, for example, an integrated Bluetooth transmitter and/or anappropriate transmitter for transmitting signals directly connected(e.g., via an audio jack) to the electronic stethoscope 501. Thetransmitter is capable of converting an analog signal to a digitalsignal and is optionally capable of encrypting the signal.

For example, heart sounds are transmitted to a processing unit 502,which can be included in a smartphone, a tablet, a computer, etc. Theprocessing unit 502 automatically analyzes the transmitted signal withthe utilization of one or more of the algorithms described above. Theanalysis yields a set of patient-specific parameters/indicators andresults, including medical and technical parameters such asheart/breathing rate, heart/breathing rate variability, systolic anddiastolic energy, signal curve, diagnosis suggestion (e.g., throughprobabilities or binary output), etc. All or a selection of theseobjective parameters and results are displayed and/or stored on theportable device as a means for diagnosis support for the medicalprofessional.

Referring to FIG. 7, a bidirectional system architecture is illustratedfor use with one or more of the algorithms described above for analyzinga signal, enabling a portable device 703 to be utilized for (i)documentation, (ii) teaching, and/or (iii) bidirectionaltele-auscultation purposes.

In reference to documentation, items 701-704 illustrate utilizing anautomated, analysis and diagnosis-support system 500 for documentationpurposes. All data and results are saved as a common file type (e.g.,PDF format) on the portable device 703, printed, and/or emailed. Abidirectional interface between the portable device 703 and the HIS 704allows for retrieving of patient data from the HIS if required by themedical professional. The bidirectional interface further allowsefficient filing of all data and results to the patient's medical file.

In reference to teaching, items 701-703 and 705 illustrate utilizing theautomated, analysis and diagnosis-support system 500 forstethoscope-based auscultation as described above. The teaching, whichis optionally directed to achieving training, research, and/orpresentation objectives, is achieved by wirelessly connecting theportable device 703 to a single or multiple other portable devices 705.The portable devices 705 receive all data including the findings of theanalysis system. According to one example, a professor teaches medicalstudents the art of auscultation by performing auscultation using theelectronic stethoscope 701 on one student and transmitting all relateddata and results of the system on the portable device 703 to theportable devices 705 of other students.

In reference to bidirectional tele-auscultation, items 701-703 and706-709 illustrate a possibility for utilizing the automated, analysisand diagnosis-support system for stethoscope-based auscultation remotelythrough bidirectional tele-auscultation. In such a scenario, the data istransmitted from the first electronic stethoscope 701 (e.g., operated bya nurse or by the patient himself), through a data link 702 to theportable device 703. The data is further transmitted through a dataconnection 706 (e.g., the Internet) to a second portable device 707(operated, e.g., by the medical professional performing theauscultation), and optionally through another data link 708 to a secondelectronic stethoscope 709 (operated, e.g., by the medical professionalperforming the auscultation).

In the above scenario, the first portable device 703 performs therequired transmitting functions (with no analysis of the signal), andthe second portable device 709 has the automated, analysis anddiagnosis-support system for stethoscope-based auscultation running. Thebidirectionality of this pathway (701-703 and 706-709) allows themedical professional operating the second portable device 707 to controlthe settings of the electronic stethoscope 701 (e.g., change filters,adjust volumes, etc.) and to communicate with the person operating theelectronic stethoscope through the first stethoscope 701 or the portabledevice 703 (e.g., instructing the person to change the position of thestethoscope). Documentation and HIS integration are options in thescenario.

Optionally, the automated analysis and diagnosis-support systemillustrated in FIG. 5 is installed and/or hosted by the HIS (e.g., HIS704 of FIG. 7) and the user accesses the HIS via a portable device(e.g., portable devices 703, 707 of FIG. 7) or via a computer connectedto the HIS. In such a case, the recorded signal data is optionallyuploaded and/or stored in the HIS and is analyzed in the HIS directlyutilizing one or more of the algorithms described above, and/or the datais downloaded onto the portable device for later or remote analysisutilizing one or more of the algorithms described above.

By way of a specific example, a medical professional uses an electronicstethoscope while medically evaluating a 10 year-old patient. Theelectronic stethoscope includes a communication input for connectingwith a portable device. The medical professional connects the portabledevice (e.g., a smartphone) (a) to the hospital information system toreceive the patient's medical data (e.g., age, medical history, etc.)and (b) to the electronic stethoscope. The communication input of theelectronic stethoscope includes, for example, a built-in Bluetooth chipor an external Bluetooth transmitter connected an audio jack.

During the medical evaluation in which auscultation is performed, theelectronic stethoscope records an acoustic heart signal while themedical professional listens to the heart sound. The electronicstethoscope converts the acoustic heart signal from an analog formatinto a digital format and transfers the digitized signal to thesmartphone. The smartphone receives the digitized signal and processesit, including removing the DC component, filtering, etc.

After the processing of the digitized signal, the smartphone estimatesthe heart rate by partitioning the signal, auto-correlating theindividual parts, applying a tapering function to each autocorrelation,and statistically analyzing the maxima of all autocorrelations. Then,the heart rate serves as the input for the segmentation stage, where arepresentative template (e.g., a template pre-stored on the smartphone)is correlated with the digitized signal to find the best matches of thistemplate in the digitized signal within defined search windows.

The segmentation results from the previous modules are used in a featureextraction stage, where characteristic properties (or features) from thedigitized signal are extracted (e.g., via Fourier Transform, GaussianMixture Models, Energy Analysis, etc.). These features serve as theinput for a decision stage, in which the features are classified, forexample, via Multilayer Perceptrons, Support Vector Machines, and/or acombination/cascade of such classifiers. The classifiers yield adiagnosis suggestion and/or a set of patient-specific parameters thatare displayed for the medical professional via the smart phone. Resultsare optionally stored on the smart phone, and/or printed, sent viae-mail, and/or stored in the hospital information system.

Each of these embodiments and obvious variations thereof is contemplatedas falling within the spirit and scope of the claimed invention, whichis set forth in the following claims. Moreover, the present conceptsexpressly include any and all combinations and subcombinations of thepreceding elements and aspects.

What is claimed is:
 1. A system for processing a quasi-periodic signal, the system comprising: an electronic stethoscope producing a quasi-periodic signal; a processor; and a memory device with stored instructions that, when executed by the processor, cause the system to: receive a representation of the quasi-periodic signal, remove a DC component from the received representation of the quasi-periodic signal to produce a purely time-varying signal, filter, the time-varying signal to produce a pre-processed signal, auto-correlate, at least a portion of a representation of the pre-processed signal with itself, store a corresponding auto-correlation output for the at least portion of the representation of the pre-processed signal, apply a biphasic tapering function to the auto-correlation output, the tapering function including a time constant parameter that is a function of the quasi-periodic signal and producing a first maximum, and store in the memory device a representation, based on the first maximum, as an indication of a rate or a frequency of the quasi-periodic signal.
 2. The system of claim 1, wherein the representation of the quasi-periodic signal includes a heartbeat and a respiration.
 3. The system of claim 1, wherein the memory device further causes the system to provide selectable settings of the electronic stethoscope and to communicate between a user of the electronic stethoscope and a medical professional.
 4. The system of claim 1, wherein the memory device further causes the system to provide an indication of the rate or the frequency of the quasi-periodic signal directly on a device selected from a group consisting of the electronic stethoscope, a second device attached to the electronic stethoscope, and a third device in wireless communication with the electronic stethoscope.
 5. The system of claim 1, wherein the memory device further causes the system to: estimate the rate or the frequency of the quasi-periodic signal; define a search window based on the estimated rate or frequency of the quasi-periodic signal; define a starting position in the received quasi-periodic signal, the starting position corresponding to the first maximum; cross-correlate a portion of the quasi-periodic signal in the search window with a template signal pattern to be matched to produce a second maximum that is defined by the controller as a new starting position; and store the new starting position.
 6. The system of claim 5, wherein the memory device further causes the system to: determine a localized pattern of the template signal pattern; extract a signal segment from the localized pattern; analyze the signal segment simultaneously in time and frequency domains to produce parallel outputs; combine the parallel outputs via a statistical or mathematical function to produce a result; and automatically indicate a diagnosis based on the result.
 7. The system of claim 6, wherein the memory device further causes the system to indicate the diagnosis directly on a device selected from a group consisting of the electronic stethoscope, a second device attached to the electronic stethoscope, and a third device in wireless communication with the electronic stethoscope.
 8. The system of claim 6, wherein the memory device further causes the system to indicate the diagnosis via one or more of a display and audio output of a portable device.
 9. The system of claim 1, wherein the memory device further causes the system to: share with one or more portable devices data associated with the representation; and display the data on displays of the one or more portable devices.
 10. A system for processing a quasi-periodic signal, the system comprising: an electronic stethoscope producing a quasi-periodic signal; a processor; and a memory device with stored instructions that, when executed by the processor, cause the system to: in response to receiving a representation of the quasi-periodic signal, produce a pre-processed time-varying signal, produce an auto-correlation output corresponding to an auto-correlation of at least a portion of a representation of the pre-processed time-varying signal with itself, in response to applying a biphasic tapering function to the auto-correlation output, estimate a frequency of the quasi-periodic signal, define a search window based on the estimated frequency of the quasi-periodic signal; define a starting position in the received quasi-periodic signal, the starting position corresponding to a first maximum; cross-correlate a portion of the quasi-periodic signal in the search window with a template signal pattern to be matched to produce a second maximum that is defined by the controller as a new starting position; and store the new starting position.
 11. The system of claim 10, wherein the electronic stethoscope includes selectable settings for communicating between a user of the electronic stethoscope and a medical professional.
 12. The system of claim 10, wherein the electronic stethoscope includes an indication of the frequency of the quasi-periodic signal.
 13. The system of claim 10, wherein the memory device further causes the system to provide an indication of the rate or the frequency of the quasi-periodic signal directly on a device selected from a group consisting of the electronic stethoscope, a second device attached to the electronic stethoscope, and a third device in wireless communication with the electronic stethoscope.
 14. The system of claim 10, wherein the memory device further causes the system to: determine a localized pattern of the template signal pattern; extract a signal segment from the localized pattern; analyze the signal segment simultaneously in time and frequency domains to produce parallel outputs; combine the parallel outputs via a statistical or mathematical function to produce a result; and automatically indicate a diagnosis based on the result.
 15. The system of claim 14, wherein the electronic stethoscope further includes at least one of a display and an audio output feature, the memory device further causing the system to indicate the diagnosis via one or more of the display and the audio output feature.
 16. The system of claim 10, wherein the electronic stethoscope includes a data connection, the memory device further causing the system to: share, via the data connection, the representation of the frequency with a hospital information system; retrieve a patient list from the hospital information system; select a patient from the patient list; obtain patient specific parameters associated with the representation of the frequency; and transfer to the hospital information system one or more of patient data, raw auscultation data, the patient specific parameters, and a diagnosis suggestion.
 17. The system of claim 10, wherein the memory device further causes the system to: send raw data of the frequency of the quasi-periodic signal to a hospital information system; analyze the raw data at the hospital information system; and store results of the analysis at the hospital information system for later review by a medical professional.
 18. The system of claim 10, wherein the memory device further causes the system to: remove a DC component from the received representation of the quasi-periodic signal to produce a purely time-varying signal, and filter, the time-varying signal to produce the pre-processed time-varying signal.
 19. The system of claim 10, wherein the tapering function including a time constant parameter that is a function of the quasi-periodic signal, the memory device further causing the system to: store the auto-correlation output; in response to applying the biphasic tapering function to the auto-correlation output, produce the first maximum; and store a representation, based on the first maximum, indicative of the frequency of the quasi-periodic signal.
 20. The system of claim 19, further comprising a portable device, the memory device further causing the system to: save the representation indicative of the frequency in a file of a predetermined file format; send the file in an e-mail; print the file; and store the file on the portable device. 